Abstract
Wafer slicing in photovoltaic industry is mainly done using multi-wire saw machines. The selection of set of bricks (parallelepiped block of crystalline silicon) to be sawn together poses difficult production scheduling decisions. The objective is to maximize the utilization of the available cutting length to improve the process throughput. We address the problem presenting a mathematical formulation and an algorithm that aims to solve it in very short running times while delivering superior solutions. The algorithm employs a reactive greedy randomized adaptive search procedure with some enhancements. Computational experiments proved its effectiveness and efficiency to solve real-world based problems and randomly generated instances. Implementation of an on-line decision system based on this algorithm can help photovoltaic industry to reduce slicing costs making a contribution for its competitiveness against other sources of energy.

Abstract
Hospital performance is a critical issue in society and its assessment must be tactfully studied in order to evaluate future decisions. In this paper we report an operating theatre study based on a simulation model, describing one of the biggest public hospitals in the north of Portugal. The model encompasses several operating rooms shared among different medical services and considers the arrival of both elective and emergency patients. We focus on a critical planning problem of the operating theatre, the allocation of medical services to operating rooms and shifts. With a discrete-event simulation model we assess the performance of the current schedule distribution and perform a set of tests in order to find a better master surgery schedule. Experiments show improvement opportunities by balancing surgical services capacities. © 2011 IEEE.

Abstract
In this paper we present a novel approach to tackling the synchronisation of a secondary resource in lot-sizing and scheduling problems. This kind of problem occurs in various manufacturing processes (e. g. wafer testing in the semiconductor industry, production and bottling of soft drinks). We consider a scenario of parallel unrelated machines that have to be equipped with a tool or need a special kind of resource for processing. Our approach allows tracing the assignment of these secondary resources across different machines and synchronising their usage independently of the time period. We present extensions of the general lot-sizing and scheduling problem and of the capacitated lot-sizing problem. We prove that the latter model is a special case of the first, but it performs computationally much better.

Abstract
The editorial section of International Journal of Production Research provides information about industrial extensions and research opportunities in the fields of lot sizing and scheduling in industries. Some of the papers published in the journal reveal that the processing industry provides significant opportunities for conducting research in these areas. The scheduling of production lots and their sizing is an area of increasing research attention within the wider field of production planning and scheduling. The close relationship between lot sizing and scheduling in many industrial applications makes it essential that these decisions are made simultaneously to use capacity efficiently. Traditional models have been increasingly refined to incorporate more detail and integrate lot sizing with scheduling. Researchers and practitioners worldwide have been making efforts to incorporate more specificities of the production environment in their models besides the integration of several independent self-contained research fields.

Abstract
The recent evidence demonstrating the importance of perishables in terms of store choice and shopping experience makes these products a very interesting topic in many different research areas. Nevertheless, the production planning research has not been paying the necessary attention to the complexities of production systems of such items. The evidence that consumers of perishable goods search for visual and other cues of freshness, such as the printed expiry dates, triggered the development of a multi-objective lot-sizing and scheduling model taking this relevant aspect into account by considering it explicitly as an objective function. A hybrid genetic algorithm based on NSGA-II was developed to allow the decision maker a true choice between different trade-offs from the Pareto front. Computational experiments were based on a case study, reported in the literature, concerning a diary company producing yogurt.

Abstract
We propose a general-purpose heuristic approach combining metaheuristics and mixed integer programming to find high quality solutions to the challenging single- and parallel-machine capacitated lotsizing and scheduling problem with sequence-dependent setup times and costs. Commercial solvers fail to solve even medium-sized instances of this NP-hard problem; therefore, heuristics are required to find competitive solutions. We develop construction, improvement and search heuristics all based on MIP formulations. We then compare the performance of these heuristics with those of two metaheuristics and other MIP-based heuristics that have been proposed in the literature, and to a state-of-the-art commercial solver. A comprehensive set of computational experiments shows the effectiveness and efficiency of the main approach, a stochastic MIP-based local search heuristic, in solving medium to large size problems. Our solution procedures are quite flexible and may easily be adapted to cope with model extensions or to address different optimization problems that arise in practice.